| Chapter title |
Application of Hidden Markov Models in Biomolecular Simulations
|
|---|---|
| Chapter number | 3 |
| Book title |
Hidden Markov Models
|
| Published in |
Methods in molecular biology, February 2017
|
| DOI | 10.1007/978-1-4939-6753-7_3 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6751-3, 978-1-4939-6753-7
|
| Authors |
Saurabh Shukla, Zahra Shamsi, Alexander S. Moffett, Balaji Selvam, Diwakar Shukla |
| Editors |
David R. Westhead, M. S. Vijayabaskar |
| Abstract |
Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 29% |
| Researcher | 4 | 24% |
| Student > Master | 3 | 18% |
| Unspecified | 1 | 6% |
| Professor > Associate Professor | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 2 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 4 | 24% |
| Chemical Engineering | 2 | 12% |
| Agricultural and Biological Sciences | 2 | 12% |
| Physics and Astronomy | 2 | 12% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Other | 4 | 24% |
| Unknown | 2 | 12% |